Online education has surpassed the conventional classroom paradigm as the new standard. The need for learning AI mobility solutions has increased as a result of COVID. For the intellectual ecosystem, it opens up a world of possibilities. Teachers increasingly use sophisticated applications and linked gadgets to teach their students. They can do this because of the combination of live video, animations, live chat, and other features.

TensorFlow is a state-of-the-art, open source machine learning framework created by Google to design, build, and train Machine Learning and Deep learning models. TensorFlow has a comprehensive and flexible ecosystem of tools and community resources that make it easy to develop and train ML and Deep Learning models. I know the options out there; prerequisites and the skills you need to acquire to overcome the learning blocks. So, Please refer to the Closing Notes section at the tail end of this piece, where you will find helpful resources for bootstrapping your intellectual abilities. My goal in this piece is to help you find some interactive courses from the Notable Educators that will edify you with a solid understanding of TensorFlow.

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind, according to ibm.com. One of the most important technologies of the 21st century, AI is already being used in dozens of processes, including speech recognition, customer service and automated stock trading. And the economic impact of AI promises to be immense. According to a 2018 report by McKinsey Global Institute, AI has the potential to add 16 percent or around $13 trillion to current global economic output by 2030. Needless to say, it is important for young people to learn about AI and take advantage of the economic and career opportunities it creates.

AI is designed to make us more efficient and productive, but study after study shows that it can also lead us to biased decision-making. This course includes invaluable lectures, articles, case studies, and ready-to-use documents to help you (and your company) get your arms around these pervasive and rapidly evolving issues. Throughout the course, we'll explain artificial intelligence in simple terms as we focus on how and where it is lurking in your HR systems. You'll acquire essential knowledge and tools to help you gain confidence in speaking about AI system-driven biases in a way that puts your HR service providers on notice and helps you hold them accountable. We'll end the course with a brief discussion on what's next in the field of AI. (Hint, hint: It includes an avalanche of legislation!)

For the code explained in each lecture, you can find a GitHub link in the resources section.

The idea of CartPole is that there is a pole standing up on top of a cart. The goal is to balance this pole by moving the cart from side to side to keep the stick balanced upright. We consider the environment won if we balance it for 500 frames and fail once the pole is tilted more than 15 degrees from totally vertical or the cart moves more than 2.4 units from the middle position. For every frame that we go with the pole "balanced" (less than 15 degrees from vertical), our "score" gets 1, and our target is a score of 500. Now, however, how can we do this?

In this work we build a unifying framework to interpolate between density-driven and geometry-based algorithms for data clustering, and specifically, to connect the mean shift algorithm with spectral clustering at discrete and continuum levels. We seek this connection through the introduction of Fokker-Planck equations on data graphs. Besides introducing new forms of mean shift algorithms on graphs, we provide new theoretical insights on the behavior of the family of diffusion maps in the large sample limit as well as provide new connections between diffusion maps and mean shift dynamics on a fixed graph. Several numerical examples illustrate our theoretical findings and highlight the benefits of interpolating density-driven and geometry-based clustering algorithms.

Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster! TensorFlow 2.0 is Google's most powerful, recently released open source platform to build and deploy AI models in practice. AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab. The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab.

Deep Learning is a subset of machine learning that encompasses neural networks that can learn from raw or unstructured data, much like humans. It's used for speech recognition, machine translation, computer vision and natural language processing. Deep Learning is at the centre of exciting innovation possibilities like Self Driven Cars, Image recognition, virtual assistants, speech recognition, machine translation, computer vision and natural language processing. Deep learning models are transforming businesses by providing learning techniques and real-world solutions based on large data sets. It is good to have skills for today's machine learning job market.

Imagine yourself being interviewed on a major TV news network and you are coming across as strong, confident and full of fascinating insights. Wouldn't you love to be a regular pundit on major news programs offering your analysis on top news stories of the day? This journalism course is delivered primarily through spoken lecture. Because the skill you are learning is speaking related, it only makes sense that you learn through speaking. The skill you will learn in this class is not primarily theoretical or academic.